Classifier Class Reference

This is the main base class that all GRT Classification algorithms should inherit from. More...

#include <Classifier.h>

Inheritance diagram for Classifier:

Public Types
enum	ClassifierModes { STANDARD_CLASSIFIER_MODE =0, TIMESERIES_CLASSIFIER_MODE }
typedef std::map< std::string, Classifier *(*)() >	StringClassifierMap
Public Types inherited from MLBase
enum	BaseType {   BASE_TYPE_NOT_SET =0, CLASSIFIER, REGRESSIFIER, CLUSTERER,   PRE_PROCSSING, POST_PROCESSING, FEATURE_EXTRACTION, CONTEXT }

Public Member Functions
	Classifier (const std::string &classifierId="")
virtual	~Classifier (void)
virtual bool	deepCopyFrom (const Classifier *classifier)
bool	copyBaseVariables (const Classifier *classifier)
virtual bool	reset ()
virtual bool	clear ()
virtual bool	computeAccuracy (const ClassificationData &data, Float &accuracy)
std::string	getClassifierType () const
bool	getSupportsNullRejection () const
bool	getNullRejectionEnabled () const
Float	getNullRejectionCoeff () const
Float	getMaximumLikelihood () const
Float	getBestDistance () const
Float	getPhase () const
Float	getTrainingSetAccuracy () const
virtual UINT	getNumClasses () const
UINT	getClassLabelIndexValue (const UINT classLabel) const
UINT	getPredictedClassLabel () const
VectorFloat	getClassLikelihoods () const
VectorFloat	getClassDistances () const
VectorFloat	getNullRejectionThresholds () const
Vector< UINT >	getClassLabels () const
Vector< MinMax >	getRanges () const
bool	enableNullRejection (const bool useNullRejection)
virtual bool	setNullRejectionCoeff (const Float nullRejectionCoeff)
virtual bool	setNullRejectionThresholds (const VectorFloat &newRejectionThresholds)
virtual bool	recomputeNullRejectionThresholds ()
bool	getTimeseriesCompatible () const
Classifier *	create () const
	GRT_DEPRECATED_MSG ("createNewInstance is deprecated, use create instead.", Classifier *createNewInstance() const )
	GRT_DEPRECATED_MSG ("createInstanceFromString is deprecated, use create instead.", static Classifier *createInstanceFromString(const std::string &id))
Classifier *	deepCopy () const
const Classifier *	getClassifierPointer () const
const Classifier &	getBaseClassifier () const
Public Member Functions inherited from MLBase
	MLBase (const std::string &id="", const BaseType type=BASE_TYPE_NOT_SET)
virtual	~MLBase (void)
bool	copyMLBaseVariables (const MLBase *mlBase)
virtual bool	train (ClassificationData trainingData)
virtual bool	train_ (ClassificationData &trainingData)
virtual bool	train (RegressionData trainingData)
virtual bool	train_ (RegressionData &trainingData)
virtual bool	train (RegressionData trainingData, RegressionData validationData)
virtual bool	train_ (RegressionData &trainingData, RegressionData &validationData)
virtual bool	train (TimeSeriesClassificationData trainingData)
virtual bool	train_ (TimeSeriesClassificationData &trainingData)
virtual bool	train (ClassificationDataStream trainingData)
virtual bool	train_ (ClassificationDataStream &trainingData)
virtual bool	train (UnlabelledData trainingData)
virtual bool	train_ (UnlabelledData &trainingData)
virtual bool	train (MatrixFloat data)
virtual bool	train_ (MatrixFloat &data)
virtual bool	predict (VectorFloat inputVector)
virtual bool	predict_ (VectorFloat &inputVector)
virtual bool	predict (MatrixFloat inputMatrix)
virtual bool	predict_ (MatrixFloat &inputMatrix)
virtual bool	map (VectorFloat inputVector)
virtual bool	map_ (VectorFloat &inputVector)
virtual bool	print () const
virtual bool	save (const std::string &filename) const
virtual bool	load (const std::string &filename)
virtual bool	save (std::fstream &file) const
virtual bool	load (std::fstream &file)
	GRT_DEPRECATED_MSG ("saveModelToFile(std::string filename) is deprecated, use save(const std::string &filename) instead", virtual bool saveModelToFile(const std::string &filename) const )
	GRT_DEPRECATED_MSG ("saveModelToFile(std::fstream &file) is deprecated, use save(std::fstream &file) instead", virtual bool saveModelToFile(std::fstream &file) const )
	GRT_DEPRECATED_MSG ("loadModelFromFile(std::string filename) is deprecated, use load(const std::string &filename) instead", virtual bool loadModelFromFile(const std::string &filename))
	GRT_DEPRECATED_MSG ("loadModelFromFile(std::fstream &file) is deprecated, use load(std::fstream &file) instead", virtual bool loadModelFromFile(std::fstream &file))
virtual bool	getModel (std::ostream &stream) const
virtual std::string	getModelAsString () const
DataType	getInputType () const
DataType	getOutputType () const
BaseType	getType () const
UINT	getNumInputFeatures () const
UINT	getNumInputDimensions () const
UINT	getNumOutputDimensions () const
UINT	getMinNumEpochs () const
UINT	getMaxNumEpochs () const
UINT	getBatchSize () const
UINT	getNumRestarts () const
UINT	getValidationSetSize () const
UINT	getNumTrainingIterationsToConverge () const
Float	getMinChange () const
Float	getLearningRate () const
Float	getRMSTrainingError () const
	GRT_DEPRECATED_MSG ("getRootMeanSquaredTrainingError() is deprecated, use getRMSTrainingError() instead", Float getRootMeanSquaredTrainingError() const )
Float	getTotalSquaredTrainingError () const
Float	getRMSValidationError () const
Float	getValidationSetAccuracy () const
VectorFloat	getValidationSetPrecision () const
VectorFloat	getValidationSetRecall () const
bool	getUseValidationSet () const
bool	getRandomiseTrainingOrder () const
bool	getTrained () const
	GRT_DEPRECATED_MSG ("getModelTrained() is deprecated, use getTrained() instead", bool getModelTrained() const )
bool	getConverged () const
bool	getScalingEnabled () const
bool	getIsBaseTypeClassifier () const
bool	getIsBaseTypeRegressifier () const
bool	getIsBaseTypeClusterer () const
bool	getTrainingLoggingEnabled () const
bool	getTestingLoggingEnabled () const
bool	enableScaling (const bool useScaling)
bool	setMaxNumEpochs (const UINT maxNumEpochs)
bool	setBatchSize (const UINT batchSize)
bool	setMinNumEpochs (const UINT minNumEpochs)
bool	setNumRestarts (const UINT numRestarts)
bool	setMinChange (const Float minChange)
bool	setLearningRate (const Float learningRate)
bool	setUseValidationSet (const bool useValidationSet)
bool	setValidationSetSize (const UINT validationSetSize)
bool	setRandomiseTrainingOrder (const bool randomiseTrainingOrder)
bool	setTrainingLoggingEnabled (const bool loggingEnabled)
bool	setTestingLoggingEnabled (const bool loggingEnabled)
bool	registerTrainingResultsObserver (Observer< TrainingResult > &observer)
bool	registerTestResultsObserver (Observer< TestInstanceResult > &observer)
bool	removeTrainingResultsObserver (const Observer< TrainingResult > &observer)
bool	removeTestResultsObserver (const Observer< TestInstanceResult > &observer)
bool	removeAllTrainingObservers ()
bool	removeAllTestObservers ()
bool	notifyTrainingResultsObservers (const TrainingResult &data)
bool	notifyTestResultsObservers (const TestInstanceResult &data)
MLBase *	getMLBasePointer ()
const MLBase *	getMLBasePointer () const
Vector< TrainingResult >	getTrainingResults () const
Public Member Functions inherited from GRTBase
	GRTBase (const std::string &id="")
virtual	~GRTBase (void)
bool	copyGRTBaseVariables (const GRTBase *GRTBase)
	GRT_DEPRECATED_MSG ("getClassType is deprecated, use getId() instead!", std::string getClassType() const )
std::string	getId () const
std::string	getLastWarningMessage () const
std::string	getLastErrorMessage () const
std::string	getLastInfoMessage () const
bool	setInfoLoggingEnabled (const bool loggingEnabled)
bool	setWarningLoggingEnabled (const bool loggingEnabled)
bool	setErrorLoggingEnabled (const bool loggingEnabled)
bool	setDebugLoggingEnabled (const bool loggingEnabled)
GRTBase *	getGRTBasePointer ()
const GRTBase *	getGRTBasePointer () const
Float	scale (const Float &x, const Float &minSource, const Float &maxSource, const Float &minTarget, const Float &maxTarget, const bool constrain=false)
Float	SQR (const Float &x) const
Public Member Functions inherited from Observer< TrainingResult >
virtual void	notify (const TrainingResult &data)
Public Member Functions inherited from Observer< TestInstanceResult >
virtual void	notify (const TestInstanceResult &data)

Static Public Member Functions
static Classifier *	create (const std::string &id)
static Vector< std::string >	getRegisteredClassifiers ()
Static Public Member Functions inherited from GRTBase
static std::string	getGRTVersion (bool returnRevision=true)
static std::string	getGRTRevison ()

Protected Member Functions
bool	saveBaseSettingsToFile (std::fstream &file) const
bool	loadBaseSettingsFromFile (std::fstream &file)
Protected Member Functions inherited from MLBase
bool	saveBaseSettingsToFile (std::fstream &file) const
bool	loadBaseSettingsFromFile (std::fstream &file)

Static Protected Member Functions
static StringClassifierMap *	getMap ()

Protected Attributes
bool	supportsNullRejection
bool	useNullRejection
UINT	numClasses
UINT	predictedClassLabel
UINT	classifierMode
Float	nullRejectionCoeff
Float	maxLikelihood
Float	bestDistance
Float	phase
Float	trainingSetAccuracy
VectorFloat	classLikelihoods
VectorFloat	classDistances
VectorFloat	nullRejectionThresholds
Vector< UINT >	classLabels
Vector< MinMax >	ranges
Protected Attributes inherited from MLBase
bool	trained
bool	useScaling
bool	converged
DataType	inputType
DataType	outputType
BaseType	baseType
UINT	numInputDimensions
UINT	numOutputDimensions
UINT	numTrainingIterationsToConverge
UINT	minNumEpochs
UINT	maxNumEpochs
UINT	batchSize
UINT	validationSetSize
UINT	numRestarts
Float	learningRate
Float	minChange
Float	rmsTrainingError
Float	rmsValidationError
Float	totalSquaredTrainingError
Float	validationSetAccuracy
bool	useValidationSet
bool	randomiseTrainingOrder
VectorFloat	validationSetPrecision
VectorFloat	validationSetRecall
Random	random
Vector< TrainingResult >	trainingResults
TrainingResultsObserverManager	trainingResultsObserverManager
TestResultsObserverManager	testResultsObserverManager
TrainingLog	trainingLog
TestingLog	testingLog
Protected Attributes inherited from GRTBase
std::string	classId
	Stores the name of the class (e.g., MinDist)
DebugLog	debugLog
ErrorLog	errorLog
InfoLog	infoLog
WarningLog	warningLog

Detailed Description

This is the main base class that all GRT Classification algorithms should inherit from.

A large number of the functions in this class are virtual and simply return false as these functions must be overwridden by the inheriting class.

Definition at line 41 of file Classifier.h.

Member Typedef Documentation

typedef std::map< std::string, Classifier*(*)() > Classifier::StringClassifierMap

Defines a map between a string (which will contain the name of the classifier, such as ANBC) and a function returns a new instance of that classifier

Definition at line 268 of file Classifier.h.

Constructor & Destructor Documentation

Classifier::Classifier

(

const std::string &

classifierId = ""

)

Default Classifier Constructor

Parameters

classifierId

the id of the parent classifier (e.g., DecisionTree)

Definition at line 77 of file Classifier.cpp.

Classifier::~Classifier ( void  )

virtual

Default Classifier Destructor

Definition at line 94 of file Classifier.cpp.

Member Function Documentation

bool Classifier::clear ( )

virtual

This function clears the classifier module, removing any trained model and setting all the base variables to their default values.

Returns

returns true if the derived class was cleared succesfully, false otherwise

Reimplemented from MLBase.

Reimplemented in DTW, HMM, FiniteStateMachine, AdaBoost, ANBC, KNN, SVM, RandomForests, DecisionTree, BAG, GMM, ParticleClassifier, Softmax, SwipeDetector, and MinDist.

Definition at line 151 of file Classifier.cpp.

bool Classifier::computeAccuracy ( const ClassificationData &  data, Float &  accuracy  )

virtual

Computes the accuracy of the current model given the input dataset. The accuracy results will be stored in the accuracy parameter and will be in the range of [0., 100.0].

Parameters

data	the dataset that will be used to test the model
accuracy	the variable to which the accuracy of the model will be stored

Returns

returns true if the accuracy was computed, false otherwise

Definition at line 171 of file Classifier.cpp.

bool Classifier::copyBaseVariables

(

const Classifier *

classifier

)

This copies the Classifier base class variables from the classifier pointer to this instance.

Parameters

classifier

a pointer to a classifier from which the values will be copied to this instance

Returns

returns true if the copy was successfull, false otherwise

Definition at line 101 of file Classifier.cpp.

Classifier * Classifier::create ( const std::string &  id )

static

Creates a new classifier instance based on the input string (which should contain the name of a valid classifier such as KNN).

Parameters

id	the name of the classifier

Returns

Classifier*: a pointer to the new instance of the classifier

Definition at line 32 of file Classifier.cpp.

Classifier * Classifier::create

(

)

const

Creates a new classifier instance based on the current classifierType string value.

Returns

Classifier*: a pointer to the new instance of the classifier

Definition at line 45 of file Classifier.cpp.

Classifier * Classifier::deepCopy

(

)

const

This creates a new Classifier instance and deep copies the variables and models from this instance into the deep copy. The function will then return a pointer to the new instance. It is up to the user who calls this function to delete the dynamic instance when they are finished using it.

Returns

returns a pointer to a new Classifier instance which is a deep copy of this instance

Definition at line 49 of file Classifier.cpp.

virtual bool Classifier::deepCopyFrom ( const Classifier *  classifier )

inlinevirtual

This is the base deep copy function for the Classifier modules. This function should be overwritten by the derived class. This deep copies the variables and models from the classifier pointer to this classifier instance.

Parameters

classifier

a pointer to the Classifier base class, this should be pointing to another instance of a matching derived class

Returns

returns true if the clone was successfull, false otherwise (the Classifier base class will always return flase)

Reimplemented in DTW, AdaBoost, SVM, KNN, RandomForests, DecisionTree, ANBC, HMM, GMM, Softmax, BAG, MinDist, SwipeDetector, FiniteStateMachine, and ParticleClassifier.

Definition at line 64 of file Classifier.h.

bool Classifier::enableNullRejection

(

const bool

useNullRejection

)

Sets if the classifier should use nullRejection.

If set to true then the classifier will reject a predicted class label if the likelihood of the prediction is below (or above depending on the algorithm) the models rejectionThreshold. If a prediction is rejected then the default null class label of 0 will be returned. If set to false then the classifier will simply return the most likely predicted class.

Parameters

useNullRejection

the new null rejection state

Returns

returns true if nullRejection was updated successfully, false otherwise

Examples:

ClassificationModulesExamples/ANBCExample/ANBCExample.cpp, and ClassificationModulesExamples/KNNExample/KNNExample.cpp.

Definition at line 249 of file Classifier.cpp.

const Classifier & Classifier::getBaseClassifier

(

)

const

Returns a pointer to this classifier. This is useful for a derived class so it can get easy access to this base classifier.

Returns

Classifier&: a reference to this classifier

Definition at line 270 of file Classifier.cpp.

Float Classifier::getBestDistance

(

)

const

Returns the current bestDistance value. The bestDistance value is computed during the prediction phase and is either the minimum or maximum distance, depending on the algorithm. This value will return 0 if a prediction has not been made.

Returns

returns the current bestDistance value

Definition at line 204 of file Classifier.cpp.

VectorFloat Classifier::getClassDistances

(

)

const

Gets a Vector of the class distances from the last prediction, this will be an N-dimensional Vector, where N is the number of classes in the model. The exact form of these distances depends on the classification algorithm.

Returns

returns a Vector of the class distances from the last prediction, an empty Vector will be returned if the model has not been trained

Examples:

ClassificationModulesExamples/AdaBoostExample/AdaBoostExample.cpp, ClassificationModulesExamples/ANBCExample/ANBCExample.cpp, ClassificationModulesExamples/BAGExample/BAGExample.cpp, ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp, ClassificationModulesExamples/DTWExample/DTWExample.cpp, ClassificationModulesExamples/GMMExample/GMMExample.cpp, ClassificationModulesExamples/KNNExample/KNNExample.cpp, ClassificationModulesExamples/MinDistExample/MinDistExample.cpp, ClassificationModulesExamples/RandomForestsExample/RandomForestsExample.cpp, ClassificationModulesExamples/SoftmaxExample/SoftmaxExample.cpp, and ClassificationModulesExamples/SVMExample/SVMExample.cpp.

Definition at line 231 of file Classifier.cpp.

const Classifier * Classifier::getClassifierPointer

(

)

const

Returns a pointer to the classifier.

Returns

returns a pointer the current classifier

Definition at line 62 of file Classifier.cpp.

std::string Classifier::getClassifierType

(

)

const

Returns the classifier type as a string.

Returns

returns the classifier type as a string

Definition at line 175 of file Classifier.cpp.

UINT Classifier::getClassLabelIndexValue

(

const UINT

classLabel

)

const

Gets the index of the query classLabel in the classLabels Vector. If the query classLabel does not exist in the classLabels Vector then the function will return zero.

Parameters

classLabel

the query classLabel

Returns

returns index of the query classLabel in the classLabels Vector

Definition at line 213 of file Classifier.cpp.

Vector< UINT > Classifier::getClassLabels

(

)

const

Gets a Vector containing the label each class represents, this will be an N-dimensional Vector, where N is the number of classes in the model. This is useful if the model was trained with non-monotonically class labels (i.e. class labels such as [1, 3, 6, 9, 12] instead of [1, 2, 3, 4, 5]).

Returns

returns a Vector containing the class labels for each class, an empty Vector will be returned if the model has not been trained

Definition at line 241 of file Classifier.cpp.

VectorFloat Classifier::getClassLikelihoods

(

)

const

Gets a Vector of the class likelihoods from the last prediction, this will be an N-dimensional Vector, where N is the number of classes in the model. The exact form of these likelihoods depends on the classification algorithm.

Returns

returns a Vector of the class likelihoods from the last prediction, an empty Vector will be returned if the model has not been trained

Examples:

ClassificationModulesExamples/AdaBoostExample/AdaBoostExample.cpp, ClassificationModulesExamples/ANBCExample/ANBCExample.cpp, ClassificationModulesExamples/BAGExample/BAGExample.cpp, ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp, ClassificationModulesExamples/DTWExample/DTWExample.cpp, ClassificationModulesExamples/GMMExample/GMMExample.cpp, ClassificationModulesExamples/KNNExample/KNNExample.cpp, ClassificationModulesExamples/MinDistExample/MinDistExample.cpp, ClassificationModulesExamples/RandomForestsExample/RandomForestsExample.cpp, ClassificationModulesExamples/SoftmaxExample/SoftmaxExample.cpp, and ClassificationModulesExamples/SVMExample/SVMExample.cpp.

Definition at line 226 of file Classifier.cpp.

static StringClassifierMap* Classifier::getMap ( )

inlinestaticprotected

This function returns the classifier map, only one map should exist across all classifiers. If a map has not been created then one will be created, otherwise the current map will be returned.

Returns

returns a pointer to the classifier map

Definition at line 354 of file Classifier.h.

Float Classifier::getMaximumLikelihood

(

)

const

Returns the current maximumLikelihood value. The maximumLikelihood value is computed during the prediction phase and is the likelihood of the most likely model. This value will return 0 if a prediction has not been made.

Returns

returns the current maximumLikelihood value

Examples:

ClassificationModulesExamples/AdaBoostExample/AdaBoostExample.cpp, ClassificationModulesExamples/DTWExample/DTWExample.cpp, and ClassificationModulesExamples/MinDistExample/MinDistExample.cpp.

Definition at line 191 of file Classifier.cpp.

Float Classifier::getNullRejectionCoeff

(

)

const

Returns the current nullRejectionCoeff value. The nullRejectionCoeff parameter is a multipler controlling the null rejection threshold for each class.

Returns

returns the current nullRejectionCoeff value

Definition at line 187 of file Classifier.cpp.

bool Classifier::getNullRejectionEnabled

(

)

const

Returns true if nullRejection is enabled.

Returns

returns true if nullRejection is enabled, false otherwise

Definition at line 183 of file Classifier.cpp.

VectorFloat Classifier::getNullRejectionThresholds

(

)

const

Gets a Vector containing the null rejection thresholds for each class, this will be an N-dimensional Vector, where N is the number of classes in the model.

Returns

returns a Vector containing the null rejection thresholds for each class, an empty Vector will be returned if the model has not been trained

Definition at line 236 of file Classifier.cpp.

UINT Classifier::getNumClasses ( ) const

virtual

Gets the number of classes in trained model.

Returns

returns the number of classes in the trained model, a value of 0 will be returned if the model has not been trained

Reimplemented in SVM.

Definition at line 209 of file Classifier.cpp.

Float Classifier::getPhase

(

)

const

This function returns the estimated gesture phase from the most recent prediction. This value is only relevant if the classifier supports timeseries classification.

Returns

Float representing the gesture phase value from the most likely class from the most recent prediction

Definition at line 196 of file Classifier.cpp.

UINT Classifier::getPredictedClassLabel

(

)

const

Gets the predicted class label from the last prediction.

Returns

returns the label of the last predicted class, a value of 0 will be returned if the model has not been trained

Examples:

ClassificationModulesExamples/AdaBoostExample/AdaBoostExample.cpp, ClassificationModulesExamples/ANBCExample/ANBCExample.cpp, ClassificationModulesExamples/BAGExample/BAGExample.cpp, ClassificationModulesExamples/DecisionTreeExample/DecisionTreeExample.cpp, ClassificationModulesExamples/DTWExample/DTWExample.cpp, ClassificationModulesExamples/GMMExample/GMMExample.cpp, ClassificationModulesExamples/KNNExample/KNNExample.cpp, ClassificationModulesExamples/MinDistExample/MinDistExample.cpp, ClassificationModulesExamples/RandomForestsExample/RandomForestsExample.cpp, ClassificationModulesExamples/SoftmaxExample/SoftmaxExample.cpp, and ClassificationModulesExamples/SVMExample/SVMExample.cpp.

Definition at line 221 of file Classifier.cpp.

Vector< MinMax > Classifier::getRanges

(

)

const

Gets a Vector of the ranges used to scale the data for training and prediction, these ranges are only used if the classifier has been trained with the #useScaling flag set to true. This should be an N-dimensional Vector, where N is the number of features in your data.

Returns

returns a Vector containing the ranges used to scale the data for classification, an empty Vector will be returned if the model has not been trained

Definition at line 245 of file Classifier.cpp.

Vector< std::string > Classifier::getRegisteredClassifiers ( )

static

Returns a Vector of the names of all classifiers that have been registered with the base classifier.

Returns

Vector< string >: a Vector containing the names of the classifiers that have been registered with the base classifier

Definition at line 66 of file Classifier.cpp.

bool Classifier::getSupportsNullRejection

(

)

const

Returns true if the classifier instance supports null rejection, false otherwise.

Returns

returns true if the classifier instance supports null rejection, false otherwise

Definition at line 179 of file Classifier.cpp.

bool Classifier::getTimeseriesCompatible ( ) const

inline

Indicates if the classifier can be used to classify timeseries data. If true then the classifier can accept training data in the LabelledTimeSeriesClassificationData format.

return returns true if the classifier can be used to classify timeseries data, false otherwise

Definition at line 263 of file Classifier.h.

Float Classifier::getTrainingSetAccuracy

(

)

const

This function returns the estimated training set accuracy from the most recent round of training. This value is only relevant if the classifier has been trained.

Returns

Float representing the training set accuracy

Definition at line 200 of file Classifier.cpp.

bool Classifier::loadBaseSettingsFromFile ( std::fstream &  file )

protected

Loads the core base settings from a file.

Returns

returns true if the base settings were loaded, false otherwise

Definition at line 321 of file Classifier.cpp.

virtual bool Classifier::recomputeNullRejectionThresholds ( )

inlinevirtual

Recomputes the null rejection thresholds for each model.

Returns

returns true if the nullRejectionThresholds were updated successfully, false otherwise

Reimplemented in DTW, AdaBoost, ANBC, KNN, GMM, MinDist, and DecisionTree.

Definition at line 255 of file Classifier.h.

bool Classifier::reset ( )

virtual

This resets the classifier. This overrides the reset function in the MLBase base class.

Returns

returns true if the classifier was reset, false otherwise

Reimplemented from MLBase.

Reimplemented in DTW, HMM, FiniteStateMachine, ParticleClassifier, SwipeDetector, ANBC, and BAG.

Definition at line 132 of file Classifier.cpp.

bool Classifier::saveBaseSettingsToFile ( std::fstream &  file ) const

protected

Saves the core base settings to a file.

Returns

returns true if the base settings were saved, false otherwise

Definition at line 274 of file Classifier.cpp.

bool Classifier::setNullRejectionCoeff ( const Float  nullRejectionCoeff )

virtual

Sets the nullRejectionCoeff, this is a multipler controlling the null rejection threshold for each class.

Parameters

nullRejectionCoeff

the new null rejection value

Returns

returns true if nullRejectionCoeff was updated successfully, false otherwise

Reimplemented in KNN, AdaBoost, and MinDist.

Definition at line 254 of file Classifier.cpp.

bool Classifier::setNullRejectionThresholds ( const VectorFloat &  newRejectionThresholds )

virtual

Manually sets the nullRejectionThresholds, these are the thresholds used for null rejection for each class. This needs to be called after the model has been trained. Calling the setNullRejectionCoeff or recomputeNullRejectionThresholds functions will override these values. The size of the newRejectionThresholds Vector must match the number of classes in the model.

Parameters

newRejectionThresholds

the new rejection thresholds

Returns

returns true if nullRejectionThresholds were updated successfully, false otherwise

Definition at line 262 of file Classifier.cpp.

---

The documentation for this class was generated from the following files:

• Classifier.h
• Classifier.cpp